Widespread, ecologically relevant genetic markers developed from association mapping of climate‐related traits in Sitka spruce (<i>Picea sitchensis</i>)
Bibliographic record
Abstract
• Genecological studies in widespread tree species have revealed steep genetic clines along environmental gradients for climate-related traits. In a changing climate, the ecological and economic importance of conifers necessitates an appraisal of how molecular genetic variation shapes quantitative trait variation, and one of the most promising approaches to answer this question is association mapping. • We phenotyped a wide collection of 410 individuals of the widely distributed conifer Sitka spruce rangewide (Picea sitchensis) for budset timing and autumn cold hardiness, and genotyped these individuals for a panel of 768 single nucleotide polymorphisms (SNPs) representing > 200 expressed nuclear genes. • After correcting for population structure, associations were detected in 28 of the candidate genes, which cumulatively explained 28 and 34% of the phenotypic variance in cold hardiness and budset, respectively. Most notable among the associations were five genes putatively involved in light signal transduction, the key pathway regulating autumn growth cessation in perennials. Many SNPs with phenotypic associations were also correlated with at least one climate variable. • This study represents a significant step toward the goal of characterizing the genomic basis of adaptation to local climate in conifers, and provides an important resource for breeding and conservation genetics in a changing climate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".